IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0251204.html
   My bibliography  Save this article

Political optimizer with interpolation strategy for global optimization

Author

Listed:
  • Aijun Zhu
  • Zhanqi Gu
  • Cong Hu
  • Junhao Niu
  • Chuanpei Xu
  • Zhi Li

Abstract

Political optimizer (PO) is a relatively state-of-the-art meta-heuristic optimization technique for global optimization problems, as well as real-world engineering optimization, which mimics the multi-staged process of politics in human society. However, due to a greedy strategy during the election phase, and an inappropriate balance of global exploration and local exploitation during the party switching stage, it suffers from stagnation in local optima with a low convergence accuracy. To overcome such drawbacks, a sequence of novel PO variants were proposed by integrating PO with Quadratic Interpolation, Advance Quadratic Interpolation, Cubic Interpolation, Lagrange Interpolation, Newton Interpolation, and Refraction Learning (RL). The main contributions of this work are listed as follows. (1) The interpolation strategy was adopted to help the current global optima jump out of local optima. (2) Specifically, RL was integrated into PO to improve the diversity of the population. (3) To improve the ability of balancing exploration and exploitation during the party switching stage, a logistic model was proposed to maintain a good balance. To the best of our knowledge, PO combined with the interpolation strategy and RL was proposed here for the first time. The performance of the best PO variant was evaluated by 19 widely used benchmark functions and 30 test functions from the IEEE CEC 2014. Experimental results revealed the superior performance of the proposed algorithm in terms of exploration capacity.

Suggested Citation

  • Aijun Zhu & Zhanqi Gu & Cong Hu & Junhao Niu & Chuanpei Xu & Zhi Li, 2021. "Political optimizer with interpolation strategy for global optimization," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-40, May.
  • Handle: RePEc:plo:pone00:0251204
    DOI: 10.1371/journal.pone.0251204
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251204
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0251204&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0251204?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nagaraju Dharavat & Suresh Kumar Sudabattula & Suresh Velamuri & Sachin Mishra & Naveen Kumar Sharma & Mohit Bajaj & Elmazeg Elgamli & Mokhtar Shouran & Salah Kamel, 2022. "Optimal Allocation of Renewable Distributed Generators and Electric Vehicles in a Distribution System Using the Political Optimization Algorithm," Energies, MDPI, vol. 15(18), pages 1-25, September.
    2. Lucarelli, Giuseppe & Genovese, Matteo & Florio, Gaetano & Fragiacomo, Petronilla, 2023. "3E (energy, economic, environmental) multi-objective optimization of CCHP industrial plant: Investigation of the optimal technology and the optimal operating strategy," Energy, Elsevier, vol. 278(PA).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0251204. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.